The models are optimized by Adam [1]} optimizer, and the initial learning rate is set as \(1\times 10^{-3}\) . For the family of HRNet, each model is trained for 210 epochs and the learning rate decays to \(1\times 10^{-4}\)  and \(1\times 10^{-5}\)  at \(170th\)  and \(200th\)  epoch respectively. For the family of SimpleBaseline, each model is trained for 140 epochs and the learning rate decays to \(1\times 10^{-4}\)  and \(1\times 10^{-5}\)  at \(90th\)  and \(110th\)  epoch respectively. All models are trained and tested on 4 Tesla V100 GPUs. More details can be referred to the Github repository Posehttps://github.com/leoxiaobin/deep-high-resolution-net.pytorch.git.
